Department of Mathematics
A convex variational model for restoring blurred images with large Rician noise
© 2014, Springer Science+Business Media New York. In this paper, a new convex variational model for restoring images degraded by blur and Rician noise is proposed. The new method is inspired by previous works in which the non-convex variational model obtained by maximum a posteriori estimation has been presented. Based on the statistical property of Rician noise, we put forward to adding an additional data-fidelity term into the non-convex model, which leads to a new strictly convex model under mild condition. Due to the convexity, the solution of the new model is unique and independent of the initialization of the algorithm. We utilize a primal–dual algorithm to solve the model. Numerical results are presented in the end to demonstrate that with respect to image restoration capability and CPU-time consumption, our model outperforms some of the state-of-the-art models in both medical and natural images.
Convexity, Deblurring, Primal–dual method, Rician noise, Total variation
Source Publication Title
Journal of Mathematical Imaging and Vision
Link to Publisher's Edition
Chen, L., & Zeng, T. (2015). A convex variational model for restoring blurred images with large Rician noise. Journal of Mathematical Imaging and Vision, 53 (1), 92-111. https://doi.org/10.1007/s10851-014-0551-y